{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "37e8cfe9",
   "metadata": {},
   "source": [
    "# Tension Board 2 / Tension Board 1: Data Overview and Climbing Statistics\n",
    "\n",
    "## Purpose\n",
    "\n",
    "This notebook establishes the basic statistical landscape of the dataset before we move into hold-level analysis and predictive modelling. The main goals are:\n",
    "\n",
    "1. to understand the size and scope of the data,\n",
    "2. to compare layouts, boards, and angles at a high level,\n",
    "3. to identify broad trends in grade, popularity, and quality,\n",
    "4. to create a clean descriptive baseline for the later modelling notebooks.\n",
    "\n",
    "Throughout, I treat each climb-angle entry as a separate observation unless explicitly noted otherwise. That matters because some climbs appear at multiple angles, so a unique climb count and a climb-angle count are not always the same thing.\n",
    "\n",
    "## Outputs\n",
    "\n",
    "This notebook produces summary tables and exploratory plots that motivate the later notebooks on:\n",
    "- hold usage,\n",
    "- hold difficulty,\n",
    "- feature engineering,\n",
    "- predictive modelling,\n",
    "- and deep learning.\n",
    "\n",
    "## Notebook Structure\n",
    "1. [Setup and Imports](#setup-and-imports)\n",
    "2. [Popularity and Temporal Trends](#popularity-and-temporal-trends)\n",
    "3. [Climbing Statistics](#climbing-statistics-grades-angles-quality-and-matching)\n",
    "4. [Prolific Statistics](#prolific-statistics)\n",
    "5. [Conclusion](#conclusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "898cad20",
   "metadata": {},
   "source": [
    "## Setup and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e48e2d25",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Setup and imports\n",
    "==================================\n",
    "\"\"\"\n",
    "# Imports\n",
    "import pandas as pd\n",
    "import sqlite3\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.patches as mpatches\n",
    "import sqlite3\n",
    "\n",
    "\n",
    "# Set some display options\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 100)\n",
    "\n",
    "# Set style\n",
    "palette=['steelblue', 'coral', 'seagreen']  #(for multi-bar graphs)\n",
    "\n",
    "# Connect to the database\n",
    "DB_PATH=\"../data/kilter.db\"\n",
    "conn = sqlite3.connect(DB_PATH)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2e7b5862",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "### Query our data from the DB\n",
    "==================================\n",
    "\n",
    "We restrict ourselves to the Kilter Original, i.e., layoud_id=1.\n",
    "For some reason there is also a first ascent logged in 2006 (\"'Preliminary first draft of Val David Sans Nom Practice. Not yet tested\").\n",
    "Since the first logged ascents start in 2018, we'll just insist that the date is larger than January 1, 2016.\n",
    "\"\"\"\n",
    "\n",
    "# Query climb data\n",
    "climbs_query = \"\"\"\n",
    "SELECT\n",
    "    c.uuid,\n",
    "    c.name AS climb_name,\n",
    "    c.setter_username,\n",
    "    c.layout_id AS layout_id,\n",
    "    c.description,\n",
    "    c.is_nomatch,\n",
    "    c.is_listed,\n",
    "    l.name AS layout_name,\n",
    "    p.name AS board_name,\n",
    "    c.frames,\n",
    "    cs.angle,\n",
    "    cs.display_difficulty,\n",
    "    dg.boulder_name AS boulder_grade,\n",
    "    cs.ascensionist_count,\n",
    "    cs.quality_average,\n",
    "    cs.fa_at\n",
    "FROM climbs c\n",
    "JOIN layouts l ON c.layout_id = l.id\n",
    "JOIN products p ON l.product_id = p.id\n",
    "JOIN climb_stats cs ON c.uuid = cs.climb_uuid\n",
    "JOIN difficulty_grades dg ON ROUND(cs.display_difficulty) = dg.difficulty\n",
    "WHERE cs.display_difficulty IS NOT NULL AND c.layout_id=1 AND cs.fa_at > '2016-01-01';\n",
    "\"\"\"\n",
    "\n",
    "# Load it into a DataFrame\n",
    "df = pd.read_sql_query(climbs_query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70a88be4",
   "metadata": {},
   "source": [
    "The above query will allow us to gather basically anything we need to in order to analyze climbing statistics. We leave out information about climging holds and things like this, because they will be analyzed in a different notebook. Let's see what our DataFrame looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b0057f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uuid</th>\n",
       "      <th>climb_name</th>\n",
       "      <th>setter_username</th>\n",
       "      <th>layout_id</th>\n",
       "      <th>description</th>\n",
       "      <th>is_nomatch</th>\n",
       "      <th>is_listed</th>\n",
       "      <th>layout_name</th>\n",
       "      <th>board_name</th>\n",
       "      <th>frames</th>\n",
       "      <th>angle</th>\n",
       "      <th>display_difficulty</th>\n",
       "      <th>boulder_grade</th>\n",
       "      <th>ascensionist_count</th>\n",
       "      <th>quality_average</th>\n",
       "      <th>fa_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>002047402B6941CEA5ED7BB09FBFE14D</td>\n",
       "      <td>4/26 Harder Than It Should Be</td>\n",
       "      <td>kilterjackie</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>p1145r12p1146r12p1149r13p1186r13p1201r13p1256r...</td>\n",
       "      <td>40</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6b/V4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-04-27 20:24:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>002ED50792A94E5EB2127D59E167B2EE</td>\n",
       "      <td>what kind of triangle</td>\n",
       "      <td>bctyner</td>\n",
       "      <td>1</td>\n",
       "      <td>Make sure you angle your body just right</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>p1123r12p1139r13p1155r12p1171r13p1187r13p1203r...</td>\n",
       "      <td>45</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7b+/V8</td>\n",
       "      <td>2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2020-01-05 17:57:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>004FB7A0C0754DA98634C5EE4D985D9A</td>\n",
       "      <td>SpEeD cLiMbInG</td>\n",
       "      <td>jefferoni</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>p1081r15p1090r13p1091r13p1094r12p1097r13p1098r...</td>\n",
       "      <td>40</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8a+/V12</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2021-03-18 02:49:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>004FB7A0C0754DA98634C5EE4D985D9A</td>\n",
       "      <td>SpEeD cLiMbInG</td>\n",
       "      <td>jefferoni</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>p1081r15p1090r13p1091r13p1094r12p1097r13p1098r...</td>\n",
       "      <td>45</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7a/V6</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2021-03-18 02:48:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00683d10a8e246b3a106531c8573f13c</td>\n",
       "      <td>marks 1 try</td>\n",
       "      <td>mark.nalder</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>p1141r15p1142r12p1144r15p1179r12p1192r13p1246r...</td>\n",
       "      <td>30</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5b/V1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2021-01-20 14:27:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302504</th>\n",
       "      <td>F32C9281901C44ADB2CF0E645416EAD4</td>\n",
       "      <td>bobbyjoy001</td>\n",
       "      <td>Sirmatt279</td>\n",
       "      <td>1</td>\n",
       "      <td>Very possible to keep tension whole way throug...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>p1075r15p1171r12p1208r12p1257r13p1292r13p1305r...</td>\n",
       "      <td>40</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6a+/V3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2026-01-31 00:51:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302505</th>\n",
       "      <td>2E9884E9AF2442D9988C90AC21B00BEA</td>\n",
       "      <td>Deuce Patrol</td>\n",
       "      <td>aaf63</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>p1082r15p1149r12p1215r13p1268r13p1353r14p1453r...</td>\n",
       "      <td>45</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6b/V4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2026-01-31 00:57:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302506</th>\n",
       "      <td>6B8AFFD9C84C42EB98F26654A3EC78C0</td>\n",
       "      <td>bobbyjoy002</td>\n",
       "      <td>Sirmatt279</td>\n",
       "      <td>1</td>\n",
       "      <td>Easing you in</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>p1076r15p1106r15p1154r15p1157r15p1166r15p1190r...</td>\n",
       "      <td>40</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6a/V3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2026-01-31 00:58:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302507</th>\n",
       "      <td>A9ED28ABDBD6449FAE4B023664904A9F</td>\n",
       "      <td>bobbyjoy003</td>\n",
       "      <td>Sirmatt279</td>\n",
       "      <td>1</td>\n",
       "      <td>Tester. No matching.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>p1159r15p1204r15p1212r12p1213r12p1247r13p1249r...</td>\n",
       "      <td>40</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6b+/V4</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2026-01-31 01:05:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302508</th>\n",
       "      <td>D3849EE4641A401EA6D7B2FF2B44C152</td>\n",
       "      <td>last minute jumps</td>\n",
       "      <td>ducky777</td>\n",
       "      <td>1</td>\n",
       "      <td>No matching.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>Kilter Board Original</td>\n",
       "      <td>p1142r15p1163r12p1176r12p1199r13p1269r13p1301r...</td>\n",
       "      <td>45</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6c+/V5</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2026-01-31 01:10:15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>302509 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    uuid                     climb_name  \\\n",
       "0       002047402B6941CEA5ED7BB09FBFE14D  4/26 Harder Than It Should Be   \n",
       "1       002ED50792A94E5EB2127D59E167B2EE          what kind of triangle   \n",
       "2       004FB7A0C0754DA98634C5EE4D985D9A                 SpEeD cLiMbInG   \n",
       "3       004FB7A0C0754DA98634C5EE4D985D9A                 SpEeD cLiMbInG   \n",
       "4       00683d10a8e246b3a106531c8573f13c                    marks 1 try   \n",
       "...                                  ...                            ...   \n",
       "302504  F32C9281901C44ADB2CF0E645416EAD4                    bobbyjoy001   \n",
       "302505  2E9884E9AF2442D9988C90AC21B00BEA                   Deuce Patrol   \n",
       "302506  6B8AFFD9C84C42EB98F26654A3EC78C0                    bobbyjoy002   \n",
       "302507  A9ED28ABDBD6449FAE4B023664904A9F                    bobbyjoy003   \n",
       "302508  D3849EE4641A401EA6D7B2FF2B44C152              last minute jumps   \n",
       "\n",
       "       setter_username  layout_id  \\\n",
       "0         kilterjackie          1   \n",
       "1              bctyner          1   \n",
       "2            jefferoni          1   \n",
       "3            jefferoni          1   \n",
       "4          mark.nalder          1   \n",
       "...                ...        ...   \n",
       "302504      Sirmatt279          1   \n",
       "302505           aaf63          1   \n",
       "302506      Sirmatt279          1   \n",
       "302507      Sirmatt279          1   \n",
       "302508        ducky777          1   \n",
       "\n",
       "                                              description  is_nomatch  \\\n",
       "0                                                                   0   \n",
       "1                Make sure you angle your body just right           0   \n",
       "2                                                                   0   \n",
       "3                                                                   0   \n",
       "4                                                                   0   \n",
       "...                                                   ...         ...   \n",
       "302504  Very possible to keep tension whole way throug...           1   \n",
       "302505                                                              0   \n",
       "302506                                      Easing you in           0   \n",
       "302507                               Tester. No matching.           1   \n",
       "302508                                       No matching.           1   \n",
       "\n",
       "        is_listed            layout_name             board_name  \\\n",
       "0               0  Kilter Board Original  Kilter Board Original   \n",
       "1               0  Kilter Board Original  Kilter Board Original   \n",
       "2               0  Kilter Board Original  Kilter Board Original   \n",
       "3               0  Kilter Board Original  Kilter Board Original   \n",
       "4               0  Kilter Board Original  Kilter Board Original   \n",
       "...           ...                    ...                    ...   \n",
       "302504          1  Kilter Board Original  Kilter Board Original   \n",
       "302505          1  Kilter Board Original  Kilter Board Original   \n",
       "302506          1  Kilter Board Original  Kilter Board Original   \n",
       "302507          1  Kilter Board Original  Kilter Board Original   \n",
       "302508          1  Kilter Board Original  Kilter Board Original   \n",
       "\n",
       "                                                   frames  angle  \\\n",
       "0       p1145r12p1146r12p1149r13p1186r13p1201r13p1256r...     40   \n",
       "1       p1123r12p1139r13p1155r12p1171r13p1187r13p1203r...     45   \n",
       "2       p1081r15p1090r13p1091r13p1094r12p1097r13p1098r...     40   \n",
       "3       p1081r15p1090r13p1091r13p1094r12p1097r13p1098r...     45   \n",
       "4       p1141r15p1142r12p1144r15p1179r12p1192r13p1246r...     30   \n",
       "...                                                   ...    ...   \n",
       "302504  p1075r15p1171r12p1208r12p1257r13p1292r13p1305r...     40   \n",
       "302505  p1082r15p1149r12p1215r13p1268r13p1353r14p1453r...     45   \n",
       "302506  p1076r15p1106r15p1154r15p1157r15p1166r15p1190r...     40   \n",
       "302507  p1159r15p1204r15p1212r12p1213r12p1247r13p1249r...     40   \n",
       "302508  p1142r15p1163r12p1176r12p1199r13p1269r13p1301r...     45   \n",
       "\n",
       "        display_difficulty boulder_grade  ascensionist_count  quality_average  \\\n",
       "0                     18.0         6b/V4                   1              1.0   \n",
       "1                     25.0        7b+/V8                   2              2.5   \n",
       "2                     29.0       8a+/V12                   1              1.0   \n",
       "3                     22.0         7a/V6                   1              3.0   \n",
       "4                     14.0         5b/V1                   2              1.5   \n",
       "...                    ...           ...                 ...              ...   \n",
       "302504                17.0        6a+/V3                   1              3.0   \n",
       "302505                18.0         6b/V4                   1              2.0   \n",
       "302506                16.0         6a/V3                   1              3.0   \n",
       "302507                19.0        6b+/V4                   1              3.0   \n",
       "302508                21.0        6c+/V5                   1              3.0   \n",
       "\n",
       "                      fa_at  \n",
       "0       2018-04-27 20:24:44  \n",
       "1       2020-01-05 17:57:46  \n",
       "2       2021-03-18 02:49:18  \n",
       "3       2021-03-18 02:48:26  \n",
       "4       2021-01-20 14:27:43  \n",
       "...                     ...  \n",
       "302504  2026-01-31 00:51:43  \n",
       "302505  2026-01-31 00:57:01  \n",
       "302506  2026-01-31 00:58:15  \n",
       "302507  2026-01-31 01:05:58  \n",
       "302508  2026-01-31 01:10:15  \n",
       "\n",
       "[302509 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7eb46cc",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4fc956",
   "metadata": {},
   "source": [
    "# Popularity and Temporal Trends\n",
    "\n",
    "## Popularity of Tension Board\n",
    "\n",
    "Since we do not have access to user data, we will examine the popular of the Tension Boards by counting first ascents and unique setters by year. Often it's the case that the first ascensionist is the also the setter of the climb, but not always. None the less, we group up first ascensionists by year, with an extra tidbit about how many unique setters there were. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3dff1bd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Popularity of Kilter board by year. \n",
    "First ascents by year + unique setters by year\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "# Convert df['fa_at'] to datetime format. (For some reason, it does not register as such)\n",
    "df['fa_at'] = pd.to_datetime(df['fa_at'])\n",
    "\n",
    "# Add a new column for the year\n",
    "df['fa_year'] = df['fa_at'].dt.year\n",
    "\n",
    "# Make a new DataFrame with year, first_ascents, and unique_setters\n",
    "df_growth = df.groupby('fa_year').agg(\n",
    "    first_ascents=('uuid', 'count'),\n",
    "    unique_setters=('setter_username', 'nunique')\n",
    ").reset_index()\n",
    "\n",
    "# Disregard the year 2026 since the data only goes one month in. \n",
    "df_growth = df_growth[df_growth['fa_year'] < 2026]\n",
    "\n",
    "# Convert year to string so that matplotlib doesn't think the year is continuous\n",
    "df_growth['fa_year'] = df_growth['fa_year'].astype(str)\n",
    "\n",
    "## Plot\n",
    "# Dual index plotting\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(12,6))\n",
    "\n",
    "# Bar chart for first ascents\n",
    "ax1.bar(df_growth['fa_year'], df_growth['first_ascents'], label='First Ascents', color='coral')\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.set_ylabel('First Ascents')\n",
    "ax1.set_title('TB First Ascents & Unique Setters over Time')\n",
    "#ax1.tick_params(axis='y')\n",
    "\n",
    "# Line chart for unique setters (secondary axis)\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(df_growth['fa_year'], df_growth['unique_setters'], color='steelblue', marker='o', label='Unique Setters')\n",
    "ax2.set_ylabel('Unique Setters', color='steelblue')\n",
    "ax2.tick_params(axis='y', labelcolor='steelblue')\n",
    "\n",
    "# Other stuff\n",
    "fig.legend(loc='upper left', bbox_to_anchor=(0.15,0.85))\n",
    "\n",
    "plt.xticks()\n",
    "plt.savefig('../images/01_climb_stats/first_ascents_by_year.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2bca15a",
   "metadata": {},
   "source": [
    "## Seasonal analysis\n",
    "\n",
    "Next, we examine when the Tension board is most popular. Again, we will work with what we have and use first ascent data. We will plot first ascents by month, combing all years. We exclude the year 2026 because this can skew the analysis as some of the month of January has data (and clearly, 2026 is when the TB2 is the most popular, so this can actually add quite a bit bias). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8912306b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Season analysis: first ascents by month\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "# First let us add a column for the month to our data\n",
    "df['fa_month'] = df['fa_at'].dt.month\n",
    "\n",
    "# Filter to years < 2026 since the data only goes one month in\n",
    "df_filter = df[df['fa_year'] < 2026]\n",
    "\n",
    "# Make a new DataFrame with month and first ascents\n",
    "df_season = df_filter.groupby('fa_month').agg(\n",
    "    first_ascents=('uuid', 'count'),\n",
    ").reset_index()\n",
    "\n",
    "# We also add a column for the month name. \n",
    "month_names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
    "               'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "df_season['fa_month_name'] = df_season['fa_month'].apply(lambda x: month_names[x-1])\n",
    "\n",
    "# Plot the data\n",
    "fig,ax = plt.subplots(figsize=(12,6))\n",
    "ax.bar(df_season['fa_month_name'], df_season['first_ascents'], color='coral')\n",
    "ax.set_title('First Ascents by Month (All Years Combined)')\n",
    "ax.set_xlabel('Month')\n",
    "ax.set_ylabel('Total First Ascents')\n",
    "\n",
    "# Save the file\n",
    "plt.savefig('../images/01_climb_stats/first_ascents_by_month.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb802d74",
   "metadata": {},
   "source": [
    "This should be what we expect: that the winter months (Dec) see the most traffic. This is probably when the outdoor climbers are hitting the boards because they're stuck inside. The warmer months see the least number of first ascents since the strong climbers are probably outdoors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def962ba",
   "metadata": {},
   "source": [
    "## Day of Week Analysis\n",
    "\n",
    "We can plot the number of first ascents by day of week. Removing the 2026 data shouldn't make a difference here, so we opt to keep it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "071cfd5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Day of Week analysis\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "# Let us add a column in our DataFrame for the day of the week.\n",
    "# Note that df.dt.day_of_week will have Monday be 0 and Sunday be 6. \n",
    "\n",
    "df['fa_day_of_week'] = df['fa_at'].dt.day_of_week\n",
    "\n",
    "\n",
    "# Make a new DataFrame with month and first ascents\n",
    "df_days = df.groupby('fa_day_of_week').agg(\n",
    "    first_ascents=('uuid', 'count'),\n",
    ").reset_index()\n",
    "\n",
    "# We also add a column for the month name. \n",
    "day_names = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']\n",
    "df_days['fa_day_name'] = df_days['fa_day_of_week'].apply(lambda x: day_names[x])\n",
    "\n",
    "# Plot the data\n",
    "fig,ax = plt.subplots(figsize=(12,6))\n",
    "ax.bar(df_days['fa_day_name'], df_days['first_ascents'], color='coral')\n",
    "ax.set_title('First Ascents by Day of Week (All Years Combined)')\n",
    "ax.set_xlabel('Day')\n",
    "ax.set_ylabel('Total First Ascents')\n",
    "\n",
    "# Save the file\n",
    "plt.savefig('../images/01_climb_stats/first_ascents_by_day_of_week.png')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9961e92e",
   "metadata": {},
   "source": [
    "Interesting, Tuesday and Wednesday have the most traffic, while Monday is the least popular."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "440a0b28",
   "metadata": {},
   "source": [
    "## Time of Day Analysis\n",
    "\n",
    "We can even do a time of day analysis. Again, we will keep the 2026 data since it shouldn't affect much. It is not entirely clear that makes sense to look at this, as we don't know if the time of first ascent is recorded in local time of the climber or local time of the server. These boards are all over the world, so this may add quite a bit of variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b8e3c4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Time of Day analysis\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "# Let us add a column in our DataFrame for the day of the week.\n",
    "# Note that df.dt.day_of_week will have Monday be 0 and Sunday be 6. \n",
    "\n",
    "df['fa_hour'] = df['fa_at'].dt.hour\n",
    "\n",
    "\n",
    "# Make a new DataFrame with month and first ascents\n",
    "df_hour = df.groupby('fa_hour').agg(\n",
    "    first_ascents=('uuid', 'count'),\n",
    ").reset_index()\n",
    "\n",
    "\n",
    "# Plot the data\n",
    "fig,ax = plt.subplots(figsize=(12,6))\n",
    "ax.bar(df_hour['fa_hour'], df_hour['first_ascents'], color='coral')\n",
    "ax.set_title('First Ascents by Hour (All Years Combined)')\n",
    "ax.set_xlabel('Hour')\n",
    "ax.set_ylabel('Total First Ascents')\n",
    "\n",
    "# Save the file\n",
    "plt.savefig('../images/01_climb_stats/first_ascents_by_hour.png')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ddc49b3",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "533dc4a2",
   "metadata": {},
   "source": [
    "# Climbing Statistics: Grades, Angles, Quality, and Matching\n",
    "\n",
    "We will visualize the climbing grade distribution. Recall that we have the following table of grades (with some other unlisted grades).\n",
    "\n",
    "|difficulty|boulder_name|route_name|\n",
    "|----------|------------|----------|\n",
    "|        10|4a/V0       |5b/5.9    |\n",
    "|        11|4b/V0       |5c/5.10a  |\n",
    "|        12|4c/V0       |6a/5.10b  |\n",
    "|        13|5a/V1       |6a+/5.10c |\n",
    "|        14|5b/V1       |6b/5.10d  |\n",
    "|        15|5c/V2       |6b+/5.11a |\n",
    "|        16|6a/V3       |6c/5.11b  |\n",
    "|        17|6a+/V3      |6c+/5.11c |\n",
    "|        18|6b/V4       |7a/5.11d  |\n",
    "|        19|6b+/V4      |7a+/5.12a |\n",
    "|        20|6c/V5       |7b/5.12b  |\n",
    "|        21|6c+/V5      |7b+/5.12c |\n",
    "|        22|7a/V6       |7c/5.12d  |\n",
    "|        23|7a+/V7      |7c+/5.13a |\n",
    "|        24|7b/V8       |8a/5.13b  |\n",
    "|        25|7b+/V8      |8a+/5.13c |\n",
    "|        26|7c/V9       |8b/5.13d  |\n",
    "|        27|7c+/V10     |8b+/5.14a |\n",
    "|        28|8a/V11      |8c/5.14b  |\n",
    "|        29|8a+/V12     |8c+/5.14c |\n",
    "|        30|8b/V13      |9a/5.14d  |\n",
    "|        31|8b+/V14     |9a+/5.15a |\n",
    "|        32|8c/V15      |9b/5.15b  |\n",
    "|        33|8c+/V16     |9b+/5.15c |\n",
    "\n",
    "We will use the actual difficulty in our work, and then unpack translations into boulder_name as we see fit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f986836",
   "metadata": {},
   "source": [
    "## Grade distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5fa594ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Difficulty distribution\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "grade_counts = df['boulder_grade'].value_counts()\n",
    "grade_order = df.groupby('boulder_grade')['display_difficulty'].mean().sort_values().index.tolist()\n",
    "grade_counts = grade_counts.reindex(grade_order)\n",
    "\n",
    "\n",
    "df_grades = df.groupby('boulder_grade').size().reset_index(name='count')\n",
    "\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(16, 8))\n",
    "\n",
    "sns.barplot(\n",
    "    data=df_grades,\n",
    "    x='boulder_grade',\n",
    "    y='count',\n",
    "    color='steelblue',\n",
    "    ax=ax,\n",
    "    width=0.6,\n",
    "    order=grade_order\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel('Grade', fontsize=11)\n",
    "ax.set_ylabel('Number of Climbs', fontsize=11)\n",
    "ax.set_title('Difficulty Distribution by Board Layout', fontsize=14)\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "ax.grid(axis='y', alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/01_climb_stats/difficulty_distribution.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "871bb45d",
   "metadata": {},
   "source": [
    "As a climber in North America, I tend to just use the V-grade and not look at the French grade. So let us group the V-grades together and show the distribution like that. We'll usually just stick the boulder_grade (e.g., 5c/V2) instead of grouping the V-grades though. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d7f0b911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JXnjhhUbbiHld87W6ESNG5LTTTkvv3r3z9a9/PYMGDcqXv/zlD70809rsuuuuDb5P69ata21OnQ/m8j/+4z8ya9asfOMb3/hI/XwUq/Y4WP1yVav0798/7dq1y6xZs2od++IXv5gWLWr+7ddmm22WJLVe2x9mXa+t1R+7T58+GTBgQG677bZcccUV5Q/Dr7vuumywwQY57LDDGvS4Dz/8cPl7sXTp0vzlL3/JOeeckxNOOCEvvvhi/uM//iNJsnDhwrzyyivZdttty89xdYMGDcpVV11V63t3xx135KqrrsqTTz6ZBQsW1NifZPX32Orqek2t6z24xRZbpFevXnXuu/JxlUqlTJw4MZMmTcozzzyT6urqrFy5snx8zedw9NFH59JLL80111yTPffcM0nyyCOP5Nlnn80RRxxRI9Rd1+tu1VhdrzsAAOpPqAEAwDq1adMm3/jGN3L55ZeX9wSYPXt2/vCHP2SrrbbKV7/61XLthAkT8tprr9W4/6BBg8qhRrdu3fLCCy/kb3/7W7bZZpsadeeee27OPffc5IO/ZF5zNcQqa9vT4O23384uu+yS119/PbvvvnuGDBmSjTbaKC1btsysWbNy1113ZenSpeX66urq5IO/PK/PY7z99ttJkj/96U/505/+tNb5WrRo0VqPNdSqzYk32WSTddadeuqp6dKlS/7zP/8zl156acaPH59WrVpl3333zYQJE8obhNdXQ/aNWKVLly61woDVz7Vqvj8pCxcurPH4a+ratWv+9re/1Rqvaz+IVXuKrLm5+IdZ12trzfk4+uij861vfSs33XRTjj322EyfPj0vvPBCjjrqqDpXRtRXZWVltt9++9x4442ZOXNm/vM//zOnnHJKPvOZz3zoHHXv3r1Wr+PHj8/JJ5+cTTbZJEOHDs1mm22Wdu3aJR+8/1d/j9X1vFe3rvfgqvs0Rahx/PHH58orr0yvXr0yfPjw9OjRI5WVlckHm9Gv+Ry22WabDBw4MHfccUfefvvtdO7cubyqZs1VNAsXLkyLFi3qfM9269YtLVq0+MTfCwAA/9e4/BQAAB9q1eV3Vq3OmDRpUlauXFljlUY+2GS5VCrV+Fr9L5Z32223JKl1GZeGWHNj51WuvfbavP766zn33HMzffr0XHHFFTnnnHMybty4OlcQrPrwet68ebWOvfnmm7XGVv019pgxY2o9x9W/Vl1i5uN677338vjjj6dly5a1NndeU0VFRb797W/n8ccfz/z583PnnXfm4IMPzm9+85vst99+Df4wfm1zvC5vvfVWjb92X2XVXK4eFqwKP/75z3/Wqm+sD3xXfb/q+l7mg+97fS+b9lGt67W1Znhy2GGHZaONNip/WL62D80/qlatWmXHHXfMypUryysFPmyOVo2vqvvnP/+Zc845Jz179syzzz6bm266KRdeeGHGjRuXsWPHZtmyZWt9/LpeU+t6D66rr49j3rx5+Y//+I/ssMMOeeGFFzJp0qRccMEFGTduXL7zne+s9X7HHHNMli5dmhtvvDHvvfdebrnllmy33Xbln2mrdOzYMStXrsz8+fPrfOyVK1fWeN19Eu8FAID/a4QaAAB8qD59+mSXXXbJjBkzyh8EtmzZMqNHj27QeUaPHp0WLVrkv/7rv+q1T0RDvPzyy0mS4cOH1zr20EMP1Rr74he/uNZjdY3tsssuqaioyMMPP9xIHa/b+PHjs3jx4uyzzz51rh5Ymy5duuSggw7KLbfckj333DPPP/98/vKXvyRJeY+ChoYc9bF8+fI88sgjtcZXzeWXvvSl8linTp2SpM6VEqsu37O6j9L3jjvumCR54IEHah177LHHsnjx4ho9NYV1vbbWfOx27drlG9/4Rv7nf/4n06ZNy6233poddtghu+yyS6P1s2q10arwqWPHjvnsZz+bv/zlL3V+L6ZNm1aj13/84x+prq5O//79a61EePzxx2td7u3DrOs9+Nprr2X27NkNOl99vPLKKymVShkyZEjat29f41hdfazyta99LRtvvHGuueaa3HLLLXnvvffy7W9/u1bdul53a85nPsJ7IR8EIU3xHgYAKAqhBgAA9bJqtca3v/3tvPLKK9l3333To0ePBp1jm222yUknnZR58+Zln332KQcRa2ro3gX54Br8+WDz4dXdfPPN+d3vflerfuTIkWnZsmUuvfTSGn8pvnDhwvJlsFbXvXv3jBgxIjNmzMjFF19c534Xjz76aN5///0G9766pUuX5qKLLsrZZ5+dDTbYIBdccMGH3ue///u/a/2l9/Lly8sfYq+6PFCnTp1SUVGRN95442P1uDb//u//nuXLl5dvv/DCC7nuuutSVVWVAw88sDy+8847J3VsmHzbbbeVP/hdXefOnZOkQX2PHDkyrVq1yqWXXlpjj4Tly5fntNNOSz7Y76UpnXfeeXn33XfLt998881ceumladWqVY3NpVdZtWH4yJEj8/777zfaKo0keeKJJzJ9+vS0atUqAwYMKI+PHj06y5cvz+mnn17jNf3MM89k4sSJqaqqykEHHZR8cJmodu3a5cknn6zxOl+wYEGOO+64Bvf05S9/OVtuuWXuvvvuGu/bUqmUH/3oR03ywf2qnxMzZsyosbLojTfeKL8u6tKmTZuMHj06Tz/9dM4888y0adMm3/zmN2vVrQp6zzrrrPLlvfLBz5WzzjqrRk2S7LTTTqmoqMjkyZOzZMmS8vhLL72Uyy+/vM5eOnfu3GTvYQCAIrCnBgAA9XL44YfnpJNOKu8nsSrkaKif/OQnWb58eS6//PLytep32GGHtG/fPvPmzcusWbPy+OOPp2PHjtlhhx3qfd5Ro0blwgsvzHHHHZf7778/W2yxRf785z/nvvvuy8EHH5w77rijRv3nP//5nHnmmRk7dmx22GGHjBgxIq1atcrtt9+ePn365MUXX6z1GD/72c/y4osv5tRTT80NN9yQAQMGpKqqKrNnz84TTzyRl156KXPmzKn1F+Brc9ttt+WFF15IPrjc1Kuvvppp06blrbfeSq9evXLjjTemd+/eH3qeww47LO3bt8+Xv/zlbLHFFlm+fHnuvffePPfccznssMOy+eabJ0k22GCD7LLLLnnwwQfzrW99K1tttVVatGiRkSNHlms+qh49euSdd97Jl770pey3336prq7OL3/5yyxZsiRXX311eYP5fLD5/JZbbplJkyZl9uzZ2XHHHfP888/nj3/8Y/bdd99aIdQXvvCF9OzZM5MnT0779u2z2WabpaKiIt/97nfXuorlc5/7XC688MKMGTOm/P3t0KFD7r777rzwwgs58MADm3zj8s9+9rPp3bt3vva1r2X58uX51a9+lXnz5uW8887LZz/72Vr1vXv3zm677ZYZM2akbdu2H7m/a665JlOmTEk+CMlefvnl3HXXXVm+fHnOOeecGmHkqaeemnvuuSc33HBDnn/++QwePDjz58/PLbfckuXLl+cXv/hF+XvXokWLHHvssRk/fny++MUv5oADDsjChQvz+9//PltssUV69uzZoD5Xrdrad999M2TIkBx22GHp2bNn/vjHP2bOnDnZYYcd8uc//7lB57z//vvXGlYNHTo0I0eOzNe+9rXcfvvt2XnnnTN48OC8+eabufvuu7PnnnvmlVdeWeu5jz766IwfPz5///vfc9hhh6VLly61ar761a/muOOOyxVXXFH+3pdKpdxxxx2ZPXt2jj/++Br7EG266aY57LDDMnny5PTt2zd777135s2blzvvvDN77713br/99lqPseeee+ZXv/pVDjnkkOy4445p2bJl9ttvv/Tp06dBcwUAUFglAACop29+85ulJKVu3bqVli9f/rHO9fjjj5e+/e1vl7beeutShw4dSq1bty5169atNGTIkNKll15amj9/fo36V199tZSkNHr06LWec9asWaWhQ4eWOnXqVNpwww1LAwcOLN13332liRMnlpKUJk6cWOs+V199dWm77bYrtWnTprTZZpuVTj755NL7779fSlIaOHBgrfr333+/dNFFF5X69u1b6tChQ6ldu3alLbfcsnTQQQeVfvGLX9RrXsaOHVtKUv5q0aJFqWPHjqXPf/7zpUMOOaQ0ceLE0qJFi+q87+jRo0tJSq+++mp57Gc/+1lp+PDhpS222KLUtm3bUpcuXUr9+vUrXXXVVbX6efHFF0v77rtvaaONNipVVFSUkpTuv//+Gn2tur2m+++/v5SkNHbs2BrjW2yxRWmLLbYovfXWW6Vvf/vbpa5du5YqKytLO++8c+muu+6q81yvvPJK6cADDyxtuOGGpQ4dOpQGDx5cmjlz5lp7eOSRR0oDBw4sbbjhhuV5WzUH6+r7rrvuKt+vsrKy1KdPn9L48eNrzcuHvb7W9nqoy8CBA0tJSu+//37p5JNPLm266aalNm3alLbffvvSNddcs877XnXVVaUkpW984xv1eqzVrXptrPna6tKlS2nYsGFr/V689957pX//938vbb311qU2bdqUNtpoo9I+++xTeuihh2rVLlu2rHTeeeeVttpqq1JlZWVp8803L5100kmld999t/w6qKun1V+va3rwwQdLX/3qV0vt2rUrde7cuXTooYeWXnvttfI81seq1+a6vk444YRSqVQqvfvuu6UxY8aUPvOZz5QqKytLW221Vemcc84pLVu27EO/zwMGDCglKd13333r7Oe6664r7bLLLqX27duX2rdvX9pll11K1113XZ21ixYtKh133HGlbt26lSorK0s77LBD6aabblrr+23OnDmlESNGlDbeeONSixYt1vqzDQDg/6qKUl3r5gEAAPjEHXvssfn5z3+eadOm1fiLfprfkiVLsummm2ajjTbKX/7ylzo3PwcAoOnZUwMAAGA9MH/+/PziF7/ItttuK9BYD1133XV5++23c8wxxwg0AACakT01AAAAmtE999yTJ598MrfddlsWLVqUsWPHNndLrOYnP/lJ5s+fn6uuuipdu3bNd77zneZ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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "V-Grade distribution\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "grade_to_v = {\n",
    "10: 0, 11: 0, 12: 0,\n",
    "13: 1, 14: 1,\n",
    "15: 2,\n",
    "16: 3, 17: 3,\n",
    "18: 4, 19: 4,\n",
    "20: 5, 21: 5,\n",
    "22: 6,\n",
    "23: 7,\n",
    "24: 8, 25: 8,\n",
    "26: 9,\n",
    "27: 10,\n",
    "28: 11,\n",
    "29: 12,\n",
    "30: 13,\n",
    "31: 14,\n",
    "32: 15,\n",
    "33: 16,\n",
    "}\n",
    "\n",
    "# Let's add a v_grade column and v_grade_counts\n",
    "df['v_grade'] = df['display_difficulty'].round().map(grade_to_v)\n",
    "df_v_grades = df.groupby('v_grade').size().reset_index(name='count')\n",
    "df_v_grades['v_label'] = 'V' + df_v_grades['v_grade'].astype(str)\n",
    "\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(16, 8))\n",
    "\n",
    "sns.barplot(\n",
    "    data=df_v_grades,\n",
    "    x='v_label',\n",
    "    y='count',\n",
    "    color='steelblue',\n",
    "    ax=ax,\n",
    "    width=0.6,\n",
    ")\n",
    "\n",
    "\n",
    "ax.set_xlabel('V-Grade', fontsize=11)\n",
    "ax.set_ylabel('Number of Climbs', fontsize=11)\n",
    "ax.set_title('V-Grade Distribution by Board Layout', fontsize=14)\n",
    "ax.tick_params(axis='x')\n",
    "ax.grid(axis='y', alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/01_climb_stats/v_grade_distribution.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e27f4277",
   "metadata": {},
   "source": [
    "Some key differences in grades are the angle at which the climb is. Note that climbs can be done at different angles."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6a34766",
   "metadata": {},
   "source": [
    "## Angle Distribution\n",
    "\n",
    "What about the angle distribution? Since the TB1 goes from 0 to 50 and the TB2 goes from 0 to 65 (although my local board only goes to 60?), let's do an analysis on each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7d65b6cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Angle distribution\n",
    "==================================\n",
    "\n",
    "The Kilter Board original goes to 70 degrees.\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "df_angle = df.groupby('angle').size().reset_index(name='count')\n",
    "\n",
    "# Reindex to correct order\n",
    "angle_order = sorted(df['angle'].unique())\n",
    "\n",
    "# Plot\n",
    "fix, ax = plt.subplots(figsize=(16,8))\n",
    "\n",
    "# Plot All Layouts\n",
    "sns.barplot(\n",
    "    data=df_angle,\n",
    "    x='angle',\n",
    "    y='count',\n",
    "    color='seagreen',\n",
    "    ax=ax,\n",
    "    width=0.6,\n",
    "    order=angle_order\n",
    ")\n",
    "\n",
    "\n",
    "ax.set_xlabel('Angle')\n",
    "ax.set_ylabel('Number of Climbs')\n",
    "ax.set_title('Angle Distribution by Board Layout')\n",
    "ax.grid(axis='y', alpha=0.3)\n",
    "\n",
    "\n",
    "plt.suptitle('Angle Distribution by Board Layout')\n",
    "plt.savefig('../images/01_climb_stats/angle_distribution.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a00ee20",
   "metadata": {},
   "source": [
    "Just like with Tension Boards, 40 is the most common angle. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc164cee",
   "metadata": {},
   "source": [
    "## Angle vs grade\n",
    "\n",
    "How is the distribution between angles and grades? Let's do this with a heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2023cb49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Angle vs grade\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 8))\n",
    "\n",
    "# Create mapping from difficulty to boulder_grade\n",
    "grade_mapping = df.groupby('display_difficulty')['boulder_grade'].first().to_dict()\n",
    "\n",
    "# Plot \"All Layouts\" as faint background boxes\n",
    "sns.boxplot(\n",
    "    data=df,\n",
    "    x='angle',\n",
    "    y='display_difficulty',\n",
    "    color='seagreen',\n",
    "    order=angle_order,\n",
    "    showfliers=False,\n",
    "    width=0.6,\n",
    "    ax=ax,\n",
    ")\n",
    "\n",
    "# Relabel y-axis with boulder_grades\n",
    "yticks_rounded = sorted(set(int(round(t)) for t in df['display_difficulty'].unique() if not pd.isna(t)))\n",
    "ylabels = [grade_mapping.get(t, '') for t in yticks_rounded]\n",
    "ax.set_yticks(yticks_rounded)\n",
    "ax.set_yticklabels(ylabels)\n",
    "\n",
    "\n",
    "ax.set_xlabel('Angle (degrees)', fontsize=11)\n",
    "ax.set_ylabel('Boulder Grade', fontsize=11)\n",
    "ax.set_title('Difficulty Distribution by Angle', fontsize=14)\n",
    "ax.grid(axis='y', alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/01_climb_stats/difficulty_by_angle_boxplot.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5477862",
   "metadata": {},
   "source": [
    "We see that angle is directly correlated with how difficult climbs are on average, right up untill 55 degrees. Then it tapers off."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7a5973a",
   "metadata": {},
   "source": [
    "## The Quality of a climb\n",
    "\n",
    "Next we examine the quality of a climb. First we look at how quality relates to the number of ascents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bf5bd013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Climb quality vs popularity\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "# Filter to climbs with quality ratings\n",
    "df_quality = df[(df['quality_average'].notna()) & (df['quality_average'] > 0)]\n",
    "\n",
    "# Sample for performance\n",
    "#df_sample = df_quality.sample(min(2000, len(df_quality)))\n",
    "\n",
    "g = sns.jointplot(\n",
    "    data=df_quality,\n",
    "    x='quality_average',\n",
    "    y='ascensionist_count',\n",
    "    kind='scatter',\n",
    "    color='teal',\n",
    "    height=5\n",
    ")\n",
    "\n",
    "g.ax_joint.set_xlabel('Quality Rating')\n",
    "g.ax_joint.set_ylabel('Ascensionist Count')\n",
    "g.fig.suptitle('Quality vs Popularity')\n",
    "\n",
    "plt.savefig('../images/01_climb_stats/quality_popularity.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf0ca290",
   "metadata": {},
   "source": [
    "Next we visualize the average quality vs the angle and grade, by means of a heatmap. Keep in mind that the harder the climb and steeper the angle, the less people will be doing it. So harder climbs are skewed towards people who can actually do it. The point is that, on boards, the climb quality isn't always the best metric. As such, we won't spend too much time on the quality and will only do a heatmap which takes into account all layouts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af7282b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Average quality by angle and grade\n",
    "\n",
    "# Filter to climbs with quality ratings\n",
    "df_quality = df[(df['quality_average'].notna()) & (df['quality_average'] > 0)]\n",
    "\n",
    "\n",
    "# Create pivot table\n",
    "quality_pivot = df_quality.pivot_table(\n",
    "    index='boulder_grade',\n",
    "    columns='angle',\n",
    "    values='quality_average',\n",
    "    aggfunc='mean'\n",
    ")\n",
    "quality_pivot = quality_pivot.reindex(grade_order)\n",
    "quality_pivot = quality_pivot.reindex(columns=[a for a in angle_order if a in quality_pivot.columns])\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(16, 8))\n",
    "\n",
    "sns.heatmap(\n",
    "    quality_pivot,\n",
    "    cmap='RdYlGn',\n",
    "    cbar_kws={'label': 'Avg Quality Rating'},\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "ax.set_xlabel('Angle (°)')\n",
    "ax.set_ylabel('Grade')\n",
    "ax.invert_yaxis()\n",
    "ax.set_title('Average Quality Rating by Grade and Angle (All Layouts)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/01_climb_stats/quality_heatmap.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3630b077",
   "metadata": {},
   "source": [
    "## \"Match\" vs. \"No Match\"\n",
    "\n",
    "Some setters opt to put the \"no match\" tag onto their climbs. This means that the climber is not allowed to match their hands on any hold. Let's do an analysis of the differences with regular climbs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ed1f0ec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Match vs No Match analysis\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "# Create status column\n",
    "df['status'] = df.apply(\n",
    "    lambda x: 'No Match' if (\n",
    "        pd.notna(x['description']) and 'No matching' in str(x['description'])\n",
    "    ) or x.get('is_nomatch', 0) == 1 else 'Matched',\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "# Aggregate by status only\n",
    "df_agg = df.groupby('status').agg(\n",
    "    count=('uuid', 'count'),\n",
    "    avg_ascensionists=('ascensionist_count', 'mean'),\n",
    "    avg_difficulty=('display_difficulty', 'mean')\n",
    ").reset_index()\n",
    "\n",
    "status_order = ['Matched', 'No Match']\n",
    "status_colors = {'Matched': 'teal', 'No Match': 'coral'}\n",
    "\n",
    "# Plot\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "for ax, metric, title in zip(axes, ['count', 'avg_difficulty', 'avg_ascensionists'], \n",
    "                               ['Total Climbs', 'Average Difficulty', 'Avg Ascensionists']):\n",
    "    \n",
    "    sns.barplot(\n",
    "        data=df_agg,\n",
    "        x='status',\n",
    "        y=metric,\n",
    "        hue='status',\n",
    "        legend=False,\n",
    "        order=status_order,\n",
    "        palette=status_colors,\n",
    "        ax=ax,\n",
    "        width=0.5\n",
    "    )\n",
    "    \n",
    "    ax.set_title(title, fontsize=12)\n",
    "    ax.set_xlabel('')\n",
    "    ax.grid(axis='y', alpha=0.3)\n",
    "\n",
    "# Y-axis labels for difficulty plot\n",
    "yticks = [11, 13, 15, 17, 19, 21, 23]\n",
    "ylabels = [grade_mapping.get(t, f\"V{t-10}\") for t in yticks]\n",
    "axes[1].set_yticks(yticks)\n",
    "axes[1].set_yticklabels(ylabels)\n",
    "axes[1].set_ylim(bottom=10)\n",
    "\n",
    "# Add value labels on bars\n",
    "for ax in axes:\n",
    "    for p in ax.patches:\n",
    "        if ax == axes[1]:  # Difficulty plot - show boulder_grade\n",
    "            height = p.get_height()\n",
    "            rounded_diff = round(height)\n",
    "            boulder_grade = grade_mapping.get(rounded_diff, f\"V{rounded_diff - 10}\")\n",
    "            ax.annotate(\n",
    "                boulder_grade,\n",
    "                (p.get_x() + p.get_width() / 2, height),\n",
    "                ha='center',\n",
    "                va='bottom',\n",
    "                fontsize=10,\n",
    "                fontweight='bold'\n",
    "            )\n",
    "        else:  # Other plots - show numeric values\n",
    "            fmt = f'{p.get_height():,.0f}' if ax == axes[0] else f'{p.get_height():.1f}'\n",
    "            ax.annotate(\n",
    "                fmt,\n",
    "                (p.get_x() + p.get_width() / 2, p.get_height()),\n",
    "                ha='center',\n",
    "                va='bottom',\n",
    "                fontsize=10\n",
    "            )\n",
    "\n",
    "plt.suptitle('Match vs No Match Climbs (All Layouts)', fontsize=14, y=1.02)\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/01_climb_stats/match_vs_nomatch.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f51156a",
   "metadata": {},
   "source": [
    "So we gather the following about \"no match\" climbs:\n",
    "\n",
    "- they are far fewer than \"match\" climbs,\n",
    "- they are on average harder than \"match\" climbs,\n",
    "- and that they have quite a bit less ascensionists on average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "21e12faa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>status</th>\n",
       "      <th>Matched</th>\n",
       "      <th>No Match</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>avg_ascensionists</th>\n",
       "      <td>56.16</td>\n",
       "      <td>13.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_difficulty</th>\n",
       "      <td>19.02</td>\n",
       "      <td>21.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>236234.00</td>\n",
       "      <td>66275.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "status               Matched  No Match\n",
       "avg_ascensionists      56.16     13.84\n",
       "avg_difficulty         19.02     21.75\n",
       "count              236234.00  66275.00"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Match vs No Match Summary\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "summary = df_agg.pivot_table(\n",
    "    columns='status',\n",
    "    values=['count', 'avg_difficulty', 'avg_ascensionists']\n",
    ").round(2)\n",
    "\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de8eb20e",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "754abf39",
   "metadata": {},
   "source": [
    "# Prolific statistics\n",
    "\n",
    "Here we will take note of some prolific statistics: what are the most popular climbs and who are the most popular setters?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "935c0ea3",
   "metadata": {},
   "source": [
    "## Most popular climbs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "42f93d59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>climb_name</th>\n",
       "      <th>setter_username</th>\n",
       "      <th>angle</th>\n",
       "      <th>boulder_grade</th>\n",
       "      <th>ascensionist_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>You Don’t Know Me</td>\n",
       "      <td>JustinKo</td>\n",
       "      <td>40</td>\n",
       "      <td>6a/V3</td>\n",
       "      <td>77383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Floats Your Boat</td>\n",
       "      <td>will_avelar</td>\n",
       "      <td>40</td>\n",
       "      <td>6a/V3</td>\n",
       "      <td>76569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bell of the Wall</td>\n",
       "      <td>KilterStudio</td>\n",
       "      <td>40</td>\n",
       "      <td>6b/V4</td>\n",
       "      <td>76388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lack of Faith</td>\n",
       "      <td>KilterStudio</td>\n",
       "      <td>40</td>\n",
       "      <td>6c/V5</td>\n",
       "      <td>67584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Evan Williams</td>\n",
       "      <td>juligris</td>\n",
       "      <td>40</td>\n",
       "      <td>5a/V1</td>\n",
       "      <td>57212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Drawing the Priest</td>\n",
       "      <td>griffinwhiteside</td>\n",
       "      <td>40</td>\n",
       "      <td>7a/V6</td>\n",
       "      <td>55975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>swooped</td>\n",
       "      <td>jwebxl</td>\n",
       "      <td>40</td>\n",
       "      <td>7a/V6</td>\n",
       "      <td>55638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tap Tap Tap</td>\n",
       "      <td>will_avelar</td>\n",
       "      <td>40</td>\n",
       "      <td>6a+/V3</td>\n",
       "      <td>53566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Priest Drawn</td>\n",
       "      <td>griffinwhiteside</td>\n",
       "      <td>40</td>\n",
       "      <td>6c+/V5</td>\n",
       "      <td>49587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pinball</td>\n",
       "      <td>Byrons</td>\n",
       "      <td>40</td>\n",
       "      <td>6b/V4</td>\n",
       "      <td>47250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Can You Ear Me Now?</td>\n",
       "      <td>KilterStudio</td>\n",
       "      <td>40</td>\n",
       "      <td>6b+/V4</td>\n",
       "      <td>46803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>kevin noooo</td>\n",
       "      <td>jwebxl</td>\n",
       "      <td>40</td>\n",
       "      <td>6c+/V5</td>\n",
       "      <td>45189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sour Gummi Worms</td>\n",
       "      <td>martiiiin</td>\n",
       "      <td>40</td>\n",
       "      <td>5c/V2</td>\n",
       "      <td>44771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Scared em Off</td>\n",
       "      <td>KilterStudio</td>\n",
       "      <td>40</td>\n",
       "      <td>6b+/V4</td>\n",
       "      <td>41769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>The Little Squeeze</td>\n",
       "      <td>bkbskilterboard</td>\n",
       "      <td>40</td>\n",
       "      <td>7a/V6</td>\n",
       "      <td>41115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             climb_name   setter_username  angle boulder_grade  \\\n",
       "1     You Don’t Know Me          JustinKo     40         6a/V3   \n",
       "2      Floats Your Boat       will_avelar     40         6a/V3   \n",
       "3      Bell of the Wall      KilterStudio     40         6b/V4   \n",
       "4         Lack of Faith      KilterStudio     40         6c/V5   \n",
       "5         Evan Williams          juligris     40         5a/V1   \n",
       "6    Drawing the Priest  griffinwhiteside     40         7a/V6   \n",
       "7               swooped            jwebxl     40         7a/V6   \n",
       "8           Tap Tap Tap       will_avelar     40        6a+/V3   \n",
       "9          Priest Drawn  griffinwhiteside     40        6c+/V5   \n",
       "10              Pinball            Byrons     40         6b/V4   \n",
       "11  Can You Ear Me Now?      KilterStudio     40        6b+/V4   \n",
       "12          kevin noooo            jwebxl     40        6c+/V5   \n",
       "13     Sour Gummi Worms         martiiiin     40         5c/V2   \n",
       "14        Scared em Off      KilterStudio     40        6b+/V4   \n",
       "15   The Little Squeeze   bkbskilterboard     40         7a/V6   \n",
       "\n",
       "    ascensionist_count  \n",
       "1                77383  \n",
       "2                76569  \n",
       "3                76388  \n",
       "4                67584  \n",
       "5                57212  \n",
       "6                55975  \n",
       "7                55638  \n",
       "8                53566  \n",
       "9                49587  \n",
       "10               47250  \n",
       "11               46803  \n",
       "12               45189  \n",
       "13               44771  \n",
       "14               41769  \n",
       "15               41115  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Most popular climbs\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "# The ascensionist_count column will allow us to easily deduce the top 15 climbs. \n",
    "\n",
    "# Create a DataFrame with the top 15 climbs\n",
    "df_popular_climbs = df.sort_values(by='ascensionist_count', ascending=False).head(15).reset_index(drop=True)\n",
    "\n",
    "# Appropriate index\n",
    "df_popular_climbs.index = df_popular_climbs.index + 1\n",
    "\n",
    "\n",
    "display(df_popular_climbs[['climb_name', 'setter_username', 'angle', 'boulder_grade', 'ascensionist_count']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d70aa42",
   "metadata": {},
   "source": [
    "It's unsuprising that every one of these climbs is at 40° given that 40° is the most popular angle, by a long shot.\n",
    "\n",
    "What about an angle-agnostic analysis? What are the top climbs amonst all angles?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "404c67aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>climb_name</th>\n",
       "      <th>total_ascensionists</th>\n",
       "      <th>avg_difficulty</th>\n",
       "      <th>avg_boulder_grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Floats Your Boat</td>\n",
       "      <td>189978</td>\n",
       "      <td>13.616047</td>\n",
       "      <td>5b/V1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lack of Faith</td>\n",
       "      <td>163229</td>\n",
       "      <td>18.174413</td>\n",
       "      <td>6b/V4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>swooped</td>\n",
       "      <td>150554</td>\n",
       "      <td>18.679320</td>\n",
       "      <td>6b+/V4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bell of the Wall</td>\n",
       "      <td>108296</td>\n",
       "      <td>14.111320</td>\n",
       "      <td>5b/V1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Scared em Off</td>\n",
       "      <td>108158</td>\n",
       "      <td>16.322120</td>\n",
       "      <td>6a/V3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sour Gummi Worms</td>\n",
       "      <td>107980</td>\n",
       "      <td>14.852447</td>\n",
       "      <td>5c/V2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Evan Williams</td>\n",
       "      <td>106809</td>\n",
       "      <td>13.423213</td>\n",
       "      <td>5a/V1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Priest Drawn</td>\n",
       "      <td>105561</td>\n",
       "      <td>18.146887</td>\n",
       "      <td>6b/V4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>You Don’t Know Me</td>\n",
       "      <td>98271</td>\n",
       "      <td>15.933520</td>\n",
       "      <td>6a/V3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Drawing the Priest</td>\n",
       "      <td>95097</td>\n",
       "      <td>19.594613</td>\n",
       "      <td>6c/V5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HilDawgyDawg</td>\n",
       "      <td>88297</td>\n",
       "      <td>16.365867</td>\n",
       "      <td>6a/V3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>proj braj</td>\n",
       "      <td>87256</td>\n",
       "      <td>18.352307</td>\n",
       "      <td>6b/V4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Boot Spur</td>\n",
       "      <td>83453</td>\n",
       "      <td>16.738333</td>\n",
       "      <td>6a+/V3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lament of the Steep</td>\n",
       "      <td>75232</td>\n",
       "      <td>16.449960</td>\n",
       "      <td>6a/V3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jug Skin</td>\n",
       "      <td>74233</td>\n",
       "      <td>15.726993</td>\n",
       "      <td>6a/V3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             climb_name  total_ascensionists  avg_difficulty avg_boulder_grade\n",
       "1      Floats Your Boat               189978       13.616047             5b/V1\n",
       "2         Lack of Faith               163229       18.174413             6b/V4\n",
       "3               swooped               150554       18.679320            6b+/V4\n",
       "4      Bell of the Wall               108296       14.111320             5b/V1\n",
       "5         Scared em Off               108158       16.322120             6a/V3\n",
       "6      Sour Gummi Worms               107980       14.852447             5c/V2\n",
       "7         Evan Williams               106809       13.423213             5a/V1\n",
       "8          Priest Drawn               105561       18.146887             6b/V4\n",
       "9     You Don’t Know Me                98271       15.933520             6a/V3\n",
       "10   Drawing the Priest                95097       19.594613             6c/V5\n",
       "11         HilDawgyDawg                88297       16.365867             6a/V3\n",
       "12            proj braj                87256       18.352307             6b/V4\n",
       "13            Boot Spur                83453       16.738333            6a+/V3\n",
       "14  Lament of the Steep                75232       16.449960             6a/V3\n",
       "15             Jug Skin                74233       15.726993             6a/V3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Top 15 most popular climbs (angle agnostic)\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "# Aggregate by climb_name (sum counts across all angles)\n",
    "df_agg = df.groupby(['climb_name']).agg(\n",
    "    total_ascensionists=('ascensionist_count', 'sum'),\n",
    "    avg_difficulty=('display_difficulty', 'mean')\n",
    ").reset_index()\n",
    "\n",
    "\n",
    "df_agg['avg_boulder_grade'] = df_agg['avg_difficulty'].round().astype(int).map(grade_mapping)\n",
    "\n",
    "# Sort and select top 15\n",
    "df_popular_climbs_aa = df_agg.sort_values(by='total_ascensionists', ascending=False).head(15).reset_index(drop=True)\n",
    "\n",
    "df_popular_climbs_aa.index = df_popular_climbs_aa.index + 1\n",
    "\n",
    "display(df_popular_climbs_aa)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab87caa",
   "metadata": {},
   "source": [
    "## Prolific setters\n",
    "\n",
    "Next, we will make a simple table of the most prolific setters by board."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3053ddc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>setter_username</th>\n",
       "      <th>climb_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BKBChicago</td>\n",
       "      <td>826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ZiYi1234</td>\n",
       "      <td>819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>forestfox98</td>\n",
       "      <td>774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RyanKing</td>\n",
       "      <td>653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GirthQuake</td>\n",
       "      <td>646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RappRocks</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Deathscorch17</td>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KilterStudio</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>jacobosullivan</td>\n",
       "      <td>486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>wjracer</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   setter_username  climb_count\n",
       "1       BKBChicago          826\n",
       "2         ZiYi1234          819\n",
       "3      forestfox98          774\n",
       "4         RyanKing          653\n",
       "5       GirthQuake          646\n",
       "6        RappRocks          510\n",
       "7    Deathscorch17          501\n",
       "8     KilterStudio          500\n",
       "9   jacobosullivan          486\n",
       "10         wjracer          452"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Top 10 setters\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "# Make a DataFrame for the setters\n",
    "df_agg = df.groupby(['setter_username']).agg(\n",
    "    climb_count=('uuid', 'nunique')\n",
    ").reset_index()\n",
    "\n",
    "df_setters = df_agg.sort_values(by='climb_count', ascending=False).head(10).reset_index(drop=True)\n",
    "\n",
    "df_setters.index = df_setters.index + 1\n",
    "\n",
    "display(df_setters)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19dd4fad",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "085c99ea",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "At this point we have a board-level and climb-level picture of the dataset. In particular, we now know:\n",
    "\n",
    "- how large the dataset is,\n",
    "- how the grade and angle distributions vary across layouts,\n",
    "- which climbs and setters appear most often,\n",
    "- and where simple descriptive trends begin to show up.\n",
    "\n",
    "That gives us enough context to move from *global statistics* to *hold-level structure*. The next notebook focuses on hold usage patterns and board heatmaps, where we stop asking only **how many climbs there are** and start asking **which physical parts of the board are driving those climbs**."
   ]
  }
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